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Record W3100955252 · doi:10.1017/aaq.2020.86

Isotopic Evidence for Garden Hunting and Resource Depression in the Late Woodland of Northeastern North America

2020· article· en· W3100955252 on OpenAlexaffabout
Eric Guiry, Trevor J. Orchard, Suzanne Needs‐Howarth, Paul Szpak

Bibliographic record

VenueAmerican Antiquity · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsTrent UniversityToronto ZooOccupational Cancer Research CentreUniversity of Toronto
Fundersnot available
KeywordsWoodlandSubsistence agricultureResource (disambiguation)GeographyTaxonAgricultureRange (aeronautics)ArchaeologyEcologySubsistence economyBiology

Abstract

fetched live from OpenAlex

Resource depression and garden hunting are major topics of archaeological interest, with important implications for understanding cultural and environmental change. Garden hunting is difficult to study using traditional zooarchaeological approaches, but isotopic analyses of animals may provide a marker for where and when people exploited nondomesticated animals that fed on agricultural resources. To realize the full potential of isotopic approaches for reconstructing garden hunting practices—and the impacts of agriculture on past nondomesticated animal populations more broadly—a wider range of species, encompassing many “ecological perspectives,” is needed. We use bone-collagen isotopic compositions of animals ( n = 643, 23 taxa, 39 sites) associated with the Late Woodland (~AD 900−1650) in what is now southern Ontario to test hypotheses about the extent to which animals used maize, an isotopically distinctive plant central to subsistence practices of Iroquoian-speaking peoples across the region. Results show that although some taxa—particularly those that may have been hard to control—had substantial access to maize, most did not, regardless of the animal resource requirements of local populations. Our findings suggest that this isotopic approach to detecting garden hunting will be more successful when applied to smaller-scale societies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.207

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.248
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations30
Published2020
Admission routes2
Has abstractyes

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